Zooplankton Bongo Net Data from the 2019 and 2020 Gulf of Alaska International Year of the Salmon Expeditions
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This record contains zooplankton occurrence data collected in the Gulf of Alaska (GoA) with a bongo net between February 19 - March 15, 2019, and March 14 – April 5, 2020, as part of the International Year of the Salmon High Seas Expedition. The bongo net (3 m length, 250 µm mesh, 50 cm diameter) was deployed at 58 stations, to a depth of 250 m and retrieved vertically at 1 m s-1. Volume of sea water filtered was determined using General Oceanics flowmeters and by multiplying effective distance travelled by the mouth area. After the bongo net deployment and recovery, the net was rinsed down into the cod end. Samples from one cod end were rinsed into a jar and preserved in 4 % formaldehyde for future taxonomic analysis. The other cod end was rinsed into a sieve and transferred below deck where it was subsequently size fractionated (250-500 µm, 500-1000 µm, 1000-2000 µm, 2000-4000 µm, and >4000 µm) onto pre-weighed filters. Individuals larger than 4000 µm were measured, identified to species level, and stored in individual Eppendorf tubes. Size fractionated zooplankton samples were stored on dry ice. This record contains the zooplankton occurrence data from both cod ends, identified to the lowest taxonomic rank possible.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it